Which of the following techniques is NOT commonly used in feature selection?
Practice Questions
Q1
Which of the following techniques is NOT commonly used in feature selection?
Recursive Feature Elimination
Principal Component Analysis
Random Forest Importance
K-Means Clustering
Questions & Step-by-Step Solutions
Which of the following techniques is NOT commonly used in feature selection?
Step 1: Understand what feature selection is. Feature selection is the process of choosing the most important variables (features) from a dataset to improve model performance.
Step 2: Identify common techniques used in feature selection. Some common techniques include filter methods, wrapper methods, and embedded methods.
Step 3: Recognize what K-Means Clustering is. K-Means Clustering is a technique used to group similar data points together into clusters.
Step 4: Compare K-Means Clustering with feature selection techniques. Since K-Means is used for clustering and not for selecting features, it is not a feature selection method.
Step 5: Conclude that K-Means Clustering is NOT commonly used in feature selection.
